Skip to main content
SpringerLink
Log in

Towards Extraction of Conceptual Structures from Electronic Health Records

  • Conference paper
Conceptual Structures: Leveraging Semantic Technologies (ICCS 2009)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 5662))

Included in the following conference series:

Abstract

This paper presents the general framework and the current results of a project that aims to develop a system for knowledge discovery and extraction from the texts of Electronic Health Records in Bulgarian language. The proposed hybrid approach integrates language technologies and conceptual processing. The system generates conceptual graphs encoding the patient case history, which contains templates for the patient’s diseases, symptoms and treatments. We describe simple inference in the generated graphs resource bank. Some experiments and their evaluation are presented in the article.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Boytcheva, S., Strupchanska, A., Paskaleva, E., Tcharaktchiev, D.: Some Aspects of Negation Processing in Electronic Health Records. In: Proc. of International Workshop Language and Speech Infrastructure for Information Access in the Balkan Countries, 2005, Borovets, Bulgaria, pp. 1–8 (2005)

    Google Scholar 

  2. Khoo, C.S.G., Chan, S., Niu, Y.: Extracting Causal Knowledge from a Medical Database Using Graphical Patterns. In: Proceedings of 38th Annual Meeting of the ACL, Hong Kong (2000)

    Google Scholar 

  3. Electronic Health Records Overview, National Institutes of Health and National Center for Research Resources, The MITRE Corporation, Center for Enterprise Modernization McLean, Virginia, USA (2006)

    Google Scholar 

  4. Friedman, C., Hripcsak, G.: Natural language processing and its future in medicine. Academic Medicine 74(8), 890–895 (1999)

    Article  Google Scholar 

  5. Friedman, C., Hripcsak, G., Shablinsky, I.: An evaluation of natural language processing methodologies. In: Chute, C.G. (ed.) Proceedings 1998 AMIA Annual Symposium, pp. 855–859. Hanley and Belfus, Phil (1998)

    Google Scholar 

  6. Gindl, S.: Negation Detection in Automated Medical Applications, a Survey. Vienna University of Technology, Institute of Software Technology & Interactive Systems, Asgaard-TR-2006-1 (October 2006)

    Google Scholar 

  7. Hripscak, G., Friedman, C., Alderson, P., DuMouchel, W., Johnson, S., Clayton, P.: Unlocking clinical data from narrative reports: a study of natural language processing. Annals of Internal Medicine 122, 681–688 (1995)

    Article  Google Scholar 

  8. Lee, C.H., Khoo, C., Na, J.C.: Automatic identification of treatment relations for medical ontology learning: An exploratory study. In: McIlwaine, I.C. (ed.) Knowledge Organization and the Global Information Society: Proc. of the Eighth International ISKO Conference, pp. 245–250. Ergon Verlag, Germany (2004)

    Google Scholar 

  9. Leroy, G., Chen, H., Martinez, J.D.: A Shallow Parser Based on Closed-class Words to Capture Relations in Biomedical Text. Journal of Biomedical Informatics (JBI) 36, 145–158 (2003)

    Article  Google Scholar 

  10. MedLEE - A Medical Language Extraction and Encoding System, http://lucid.cpmc.columbia.edu/medlee/

  11. Miller, G., Hristea, F.: WordNet Nouns: Classes and Instances. Computational Linguistics 32(1), 1–3 (2006)

    Article  Google Scholar 

  12. Natural Language Processing in Medical Coding. White paper of Language and Computing (April 2004), http://www.landcglobal.com

  13. Neubauer, T., Riedl, B.: Improving Patients Privacy with Pseudonymization. In: eHealth Beyond the Horizon - Get IT There, pp. 691–696. IOS Press, Amsterdam (2008)

    Google Scholar 

  14. Ruch, P., Baud, R.H., Geissbühler, A.: Using lexical disambiguation and named-entity recognition to improve spelling correction in the electronic patient record. Artificial Intelligence in Medicine 29(1-2), 169–184 (2003)

    Article  Google Scholar 

  15. Savova, G., Ogren, P., Duffy, P., Buntrock, J., Chute, C.G.: Mayo Clinic NLP System for Patient Smoking Status Identification. Journal of Americal Medical Information Association 15, 25–28 (2008)

    Article  Google Scholar 

  16. Schulz, S., Suntisrivaraporn, B., Baader, F.: SNOMED CT’s Problem List: Ontologists and Logicians Therapy Suggestions. In: Proc. of the Medinfo 2007 Congress, Studies in Health Technology and Informatics (SHTI-series) (2007)

    Google Scholar 

  17. Shimbo, M., Tamamori, S., Matsumoto, Y.: Finding cue expressions for knowledge extraction from scientific text early results. In: Pacific Knowledge Acquisition Workshop 2004, Auckland, New Zealand (2004)

    Google Scholar 

  18. Sowa, J.: Conceptual Processing in Mind and Machines, Reading, MA (1984)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. State University of Library Studies and Information Technologies, 119 Tzarigradsko Shose Blvd., 1784, Sofia, Bulgaria

    Svetla Boytcheva

  2. Institute for Parallel Processing, Bulgarian Academy of Sciences, 25A Acad. G. Bonchev Str., 1113, Sofia, Bulgaria

    Galia Angelova

Authors
  1. Svetla Boytcheva
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Galia Angelova
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Institut AIFB, Universität Karlsruhe (TH), 76128, Karlsruhe, Germany

    Sebastian Rudolph

  2. SAP Research Center CEC Dresden, Chemnitzer Strasse 48, 01187, Dresden, Germany

    Frithjof Dau

  3. Higher School of Economics, Department of Applied Mathematics and Information Science, State University, Kirpichnaya 33/5, 105679, Moscow, Russia

    Sergei O. Kuznetsov

Rights and permissions

Reprints and permissions

Copyright information

© 2009 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Boytcheva, S., Angelova, G. (2009). Towards Extraction of Conceptual Structures from Electronic Health Records. In: Rudolph, S., Dau, F., Kuznetsov, S.O. (eds) Conceptual Structures: Leveraging Semantic Technologies. ICCS 2009. Lecture Notes in Computer Science(), vol 5662. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03079-6_8

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-03079-6_8

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-03078-9

  • Online ISBN: 978-3-642-03079-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation